Skip to content

Instantly share code, notes, and snippets.

Discover gists

@retornam
retornam / resources.md
Created March 24, 2022 19:53 — forked from muff-in/resources.md
A curated list of Assembly Language / Reversing / Malware Analysis / Game Hacking-resources
@deltatrend
deltatrend / RP-Profits-8AM-ORB.txt
Last active May 22, 2026 15:56
RP Profits' 8AM ORB strategy, implemented in PineScript
//@version=6
// © QuantPad LLC [made with https://quantpad.ai/]
strategy("'RP Profits' 8AM ORB",
overlay = true,
dynamic_requests = true,
initial_capital = 50000,
default_qty_type = strategy.fixed,
default_qty_value = 2,
commission_type = strategy.commission.cash_per_contract,
commission_value = 1.40,
@karpathy
karpathy / llm-wiki.md
Created April 4, 2026 16:25
llm-wiki

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

@SXHRYU
SXHRYU / Makefile
Last active May 22, 2026 15:56
LLVM 22 MacPorts CMake setup
cmake-configure:
rm -rf build && cmake --toolchain toolchain.cmake -S . -B build
cmake-build:
cmake --build build -v
@EdwarGomez
EdwarGomez / llm-wiki.md
Created May 22, 2026 15:48 — forked from karpathy/llm-wiki.md
llm-wiki

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

@hqman
hqman / CLAUDE.md
Created February 23, 2026 13:03
Boris Cherny’s CLAUDE.md

Workflow Orchestration

1. Plan Node Default

  • Enter plan mode for ANY non-trivial task (3+ steps or architectural decisions)
  • If something goes sideways, STOP and re-plan immediately - don't keep pushing
  • Use plan mode for verification steps, not just building
  • Write detailed specs upfront to reduce ambiguity

2. Subagent Strategy

  • Use subagents liberally to keep main context window clean
@tavinus
tavinus / cloudsend.sh
Last active May 22, 2026 15:41
Send files to Nextcloud/Owncloud shared folder using curl
#!/usr/bin/env bash
############################################################
# MIGRATED TO REPOSITORY
# https://github.com/tavinus/cloudsend.sh
#
# This gist will NOT be updated anymore
############################################################
############################################################